Food: Not Only Safety, but Also Sustainability. The Emerging Trend of New Social Consumers

Consumers’ purchasing choices are no longer based only on economic factors but also on ethical reasons related to environmental sustainability and food safety. However, nutritional information on food labels is underused by consumers. Often the lack or incompleteness of information available on the market obstructs the complex transition towards sustainable consumption patterns. This empirical study analysed a sample of 359 consumers from an area in Southern Italy (city of Naples) to identify homogeneous consumer clusters with respect to the assessment of the level of consumer attention to sustainable environmental, social behaviours in daily life, and also to safety attributes. The most important sources of information influencing the consumers’ choices, food safety knowledge, and future purchasing behaviour were analysed. The research sample was self-selected, and the questionnaire for the survey was administrated through a non-probability sample from a reasoned choice. The results indicate that the ideal solution is a five-cluster partition that confirms a good level of attention to intrinsic attributes, in particular food expiry, transparency of food information, food traceability, and seller confidence. In addition, the research could provide an opportunity to consider collaborative actions between policy makers and industries to increase consumer awareness of environmental attributes.

JUSUF: Modular Tier-2 Supercomputing and Cloud Infrastructure at Jülich Supercomputing Centre

JUSUF is a petaflop supercomputer operated by Jülich Supercomputing Centre at Forschungszentrum Jülich as a European supercomputing and cloud resource. JUSUF was funded via the ICEI project and especially serves the Human Brain Project and PRACE via ICEI and the Fenix Research Infrastructure. The system consists of two parts, an HPC cluster partition and an Infrastructure-as-a-Service cloud partition. The system entered production phase in spring 2020. It is based on the Bull X400 product family with AMD Rome processors, partially accelerated by Nvidia V100 GPUs, and Nvidia Mellanox HDR InfiniBand.

Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them to partition signed networks of collaboration and opposition in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.

A New Algorithm for Identifying and Quantifying of Latent Classes

Processing large amounts of data can be greatly simplified if this data is divided into approximately homogeneous groups. Splitting into such groups is the task of cluster analysis. However, the question of constructing an objective, natural partition into clusters remains open. The paper considers a modern approach to the search for such an objective cluster structure by highlighting the indicator of a common essential part from the set of characteristics that define objects (we call them the forming ones). When this indicator is fixed, the remains of the forming characteristics become independent or close to such. The resulting independent residuals are interpreted as a kind of information noise, and the latent cluster variable, the common fixed part that provides such a transformation, can be a reason for the objective integration of objects into clusters. A new algorithm for the formation of a cluster partition based on the proximity or coincidence of the values of a latent cluster variable with the simultaneous quantification of its values is proposed. The algorithm is based on the targeted search of partitions, the transition from the start one to the partition, more close to the objective. The algorithm proposed in the paper can be easily modified to the case of non-numeric categorized characteristics.

Planning of High Renewable-Penetrated Distribution Systems Considering Complementarity and Cluster Partitioning

Photovoltaic (PV) and wind power (WT) resources can influence each other in some scenarios, and this influence tends to show that the rise of PV resources may indicate the drop of WT resources, and vice versa. This pattern of PV and WT resources influencing each other is called the complementary characteristics of PV and WT power. The complementary characteristics of the power outputs of different kinds of distributed renewable energy resources (DRERs) and the correlation between DRERs outputs and loads can impact the consumption of DRERs by the loads within the grid, which represents the rate of DRER outputs consumed by loads instead of being reduced. In this regard, this paper investigates a planning strategy for DRERs considering these two factors. An improved co-variance matrix method is applied to generate complementary samples of DRERs and correlated samples of DRERs and loads. The samples generated are used to study the impacts of the degree of correlation between DRERs and loads on the consumption ability of DRERs. The concept of the cluster is introduced as a region including DRERs with complementary characteristics. Based on the cluster partition method and the samples generated, the DRERs planning model is proposed to maximize the profits of different DRER stakeholders. The planning model is transformed into a single objective model through the ideal point method. A Benders decomposition-based method is developed to efficiently solve the proposed model, and an actual network in China is used to illustrate its performance. The results show DRER consumption can be significantly improved by the proposed planning model.